Machine-learning classifiers (e.g., neural networks), predictive models, and statistical models can apply hundreds of thousands of operations to data sets to determine information about the data sets. It can take a significant amount of time, memory, processing power, and electrical power to perform these operations. For example, a computing device may perform thousands of matrix operations over hundreds of iterations of steps to analyze a data set, during which time the computing device may repeatedly access and store information in thousands of memory locations. This level of processing can take hours, or days, to perform; can require a significant amount of electrical power; can reduce the available resources (e.g., processing power and memory) on the computing device for performing other tasks; can slow down other processes executing on the computing device; and can require complicated and expensive hardware.